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Strategies Beyond Genome-Wide Association Studies for Atherosclerosis

Seraya Maouche and Heribert Schunkert

Arterioscler Thromb Vasc Biol. 2012;32:170-181

doi: 10.1161/ATVBAHA.111.232652

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ATVB in Focus

Life After GWAS: Functional Genomics in Vascular Biology

Series Editor: Aldons J. Lusis

Strategies Beyond Genome-Wide Association Studies

for Atherosclerosis

Seraya Maouche, Heribert Schunkert

Abstract—Atherosclerotic diseases, including coronary artery disease (CAD) and myocardial infarction (MI), are the

leading causes of death in the world. The genetic basis of CAD and MI, which are caused by multiple interacting

endogenous and exogenous factors, has gained considerable interest in the last years as genome-wide association studies

(GWASs) have identified many new susceptibility loci for CAD and MI, and the underlying genes provide new insights

into the genetic architecture of these diseases. Here we summarize the recent findings from GWASs of atherosclerosis

and discuss their functional and biological implications. We also discuss the different post-GWAS strategies that are

currently used for refining the location of causal variants, understanding their role, and shedding light on molecular

mechanisms explaining their association to CAD. We finally discuss potential clinical translations of GWAS findings

for individual risk prediction, advanced clinical strategies, and personalized treatments. (Arterioscler Thromb Vasc

Biol. 2012;32:170-181.)

Key Words: atherosclerosis coronary artery disease genome-wide association studies myocardial infarction

post-GWAS

Over the last 5 years, genome-wide association studies

(GWASs) have proved to be one of the most successful

approaches by which to study the inherited basis of human

diseases. Indeed, GWASs have identified hundreds of susceptibility

genes for a large number of human conditions and

quantitative traits. 1 The April 2011 release of the National

Human Genome Institute’s catalogue of published GWASs2 contained 972 studies involving 4827 single-nucleotide polymorphisms

(SNPs) with implications for human phenotypes.

Such an impressive number of identified risk loci provides

unprecedented new insights into the genetic architecture of

various diseases, specifically if we compare this success story

with the outcome of linkage and gene-candidate analyses in

the past. However, only a small proportion of the overall

genetic variance of the traits under investigation can be

explained by the loci identified thus far. In addition, several

associations localize within noncoding regions, challenging

the interpretation of GWAS findings as causal genes, and

molecular mechanisms are difficult to elucidate. This situation

has created some skepticism about the utility of the

GWAS approach and raised a debate about the so-called

missing heritability. 3

Nevertheless, GWASs have already discovered some

cellular pathways underlying the mechanisms of complex

diseases. Lander provided examples of how this

hypothesis-free approach may advance our understanding

of pathophysiology. 4

GWASs for Atherosclerosis: Current Status

Several epidemiological, family, and twin studies have provided

evidence of genetic roots in the development of coronary artery

disease (CAD) and myocardial infarction (MI). First, positive

family history of premature CAD is an established independent

factor in prediction of clinical and subclinical disease risk. 5,6

Second, approximately 50% of unexplained variation in cardiovascular

risk can be attributed to genetic and environmental

factors shared within a family. 7 Third, human single-gene

mutations/variants underlying hypercholesterolemia have been

identified (http://omim.org/entry/143890). Fourth, genetically

modified animal models match human traits underlying CAD,

such as familial hypercholesterolemia. 7

After several years of hypothesis-driven studies, 8 the

GWAS approach has led to identification of several genomic

regions putatively harboring CAD/MI susceptibility genes. 9,10

Since the identification in 2007 of the 9p21.3 locus, 11–13

which is so far the most robustly replicated genetic signal for

CAD around the world, 14–16 several other loci have been

identified by the Wellcome Trust Case Control Consortium

12,17 ; the Ottawa Heart Study 11 ; German Myocardial

Infarction Family Studies I, 12 II, 18 and III 19 ; deCode 20 ; and

the Myocardial Infarction Genetics Consortium. 21

As of March 2011, 12 CAD loci had been identified

(Table). Since then, international efforts gathered GWAS

cohorts for CAD and MI and carried out meta-analyses

pooling hundreds of thousands of samples. Methodological

Received on: September 11, 2011; final version accepted on: November 28, 2011.

From the Universität zu Lübeck, Medizinische Klinik II, Lübeck, Germany.

Correspondence to Heribert Schunkert, Universität zu Lübeck, Medizinische Klinik II, Ratzeburger Allee 160, 23538 Lübeck, Germany. E-mail

heribert.schunkert@uksh.de

© 2012 American Heart Association, Inc.

Arterioscler Thromb Vasc Biol is available at http://atvb.ahajournals.org DOI: 10.1161/ATVBAHA.111.232652

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Table. The 32 Loci Associated With CAD/MI at Genome-Wide Levels of Statistical Significance

Locus SNP ID Genes in Region

1p13.3 rs599839 CELSR2/PSRC1/

SORT1

Risk Allele

Frequency

(Risk

Allele)

OR

(95% CI)

Association With

CAD Risk Factor

or Other Pleiotropic

Effect?*

eQTL

Effects# Biological Mechanism Reference

0.78 (A) 1.11 (1.08–1.15) Yes Yes Lipoprotein and

cholesterol

metabolisms

1p32.2 rs17114036 PPAP2B 0.91 (A) 1.17 (1.13–1.22) Yes Yes Unknown 24

1p32.3 rs11206510 PCSK9 0.82 (T) 1.08 (1.05–1.11) Yes Unknown LDL metabolism 21, 24

1q41 rs17465637 MIA3 0.74 (C) 1.14 (1.09–1.20) Yes Unknown Unknown 12, 24

2q33.1 rs6725887 WDR12 0.15 (C) 1.14 (1.09–1.19) Yes Yes Unknown 21, 24

3q22.3 rs2306374 MRAS 0.18 (C) 1.12 (1.07–1.16) Yes Yes Unknown 18, 24

6p21.31 rs17609940 ANKS1A 0.75 (G) 1.07 (1.05–1.10) Yes Unknown Unknown 24

12, 24, 26

6p24.1 rs12526453 PHACTR1 0.67 (C) 1.10 (1.06–1.13) Yes Unknown Unknown 21, 24, 26

6p24.1 rs6903956 C6orf105 0.10 (A) 1.51 (1.34–1.70) Yes Unknown Unknown 29

6q23.2 rs12190287 TCF21 0.62 (C) 1.08 (1.06–1.10) Yes Yes Unknown 24

6q25.3 rs3798220 LPA 0.02 (C) 1.51 (1.33–1.70) Yes Unknown Lipoprotein

metabolism

7q22.3 rs10953541 BCAP29 0.75 (C) 1.10 (1.05–1.15) Yes Yes Unknown 26

7q32.2 rs11556924 ZC3HC1 0.62 (C) 1.09 (1.07–1.12) Unknown Unknown Unknown 24

9p21.3 rs4977574 CDKN2A/B/

CDK2B-AS1

0.46 (G) 1.29 (1.23–1.36) Yes Yes Increase proliferation

of smooth muscle

cells

9q33 rs7025486 DAB2IP 0.14 (A) 1.09 (1.04–1.15) Yes Unknown Unknown 30

9q34.2 rs579459 ABO 0.21 (C) 1.10 (1.07–1.13) Yes Unknown Unknown 24

10p11.23 rs3739998 KIAA1462 0.43 (G) 1.10 (1.06–1.13) Yes Yes Unknown 19, 26

10q11.21 rs1746048 CXCL12 /HNRNPA3P1 0.87 (C) 1.09 (1.07–1.13) Yes Unknown Neointima formation

after arterial injury

platelet activation in

atherosclerotic lesions

10q23.31 rs1412444 LIPA 0.34 (T) 1.08 (1.05–1.12) Yes Yes Lipoprotein

metabolism

10q24.32 rs12413409 CYP17A1/

CNNM2/NT5C2

0.89 (G) 1.12 (1.08–1.16) Yes Yes Unknown 24

11q22.3 rs974819 PDGFD/ DYNC2H1 0.29 (T) 1.09 (1.05–1.13) Yes Yes Unknown 26

11q23.3 rs964184 ZNF259/ APOA5/4/1/

APOC3

0.13 (G) 1.13 (1.10–1.16) Yes Unknown Unknown 24

12q24.12 rs3184504 SH2B3 0.44 (T) 1.07 (1.04–1.10) Yes Unknown Unknown 20, 24

13q34 rs4773144 COL4A1/ COL4A2 0.44 (G) 1.07 (1.05–1.09) Yes Unknown Unknown 24

14q32.2 rs2895811 HHIPL1 0.43 (C) 1.07 (1.05–1.10) Yes Unknown Unknown 24

15q25 rs4380028 MORF4L1/ ADAMTS7/

RPL21P116

0.60 (C) 1.07 (1.03–1.11) Unknown Yes Unknown 26

15q25.1 rs3825807 ADAMTS7 0.57 (A) 1.08 (1.06–1.10) Yes Unknown Neointima formation

vascular remodeling

17p11.2 rs12936587 RASD1/SMCR3/

PEMT/ RAI1

0.56 (G) 1.07 (1.05–1.09) Yes Yes Unknown 24

17p13.3 rs216172 SMG6/SRR 0.37 (C) 1.07 (1.05–1.09) Yes Unknown Unknown 24

17q21.32 rs46522 UBE2Z/GIP/

ATP5G1/SNF8

0.53 (T) 1.06 (1.04–1.08) Yes Yes Unknown 24

19p13.2 rs1122608 LDLR/ SMARCA4 0.77 (G) 1.14 (1.09–1.18) Yes Yes Lipoprotein

metabolism

21q22.11 rs9982601 SLC5A3/MRPS6/

KCNE2/C21orf82

Maouche and Schunkert GWASs for Atherosclerosis 171

11–13, 24, 26, 27

12, 24

26, 91

24, 25

21, 24

0.15 (T) 1.18 (1.12–1.24) Yes Yes Unknown 21, 24

CAD indicates coronary artery disease; MI, myocardial infarction; SNP, single-nucleotide polymorphism; ID, identification number; OR, odds ratio; eQTL, expression

quantitative trait locus; LDL, low-density lipoprotein.

*Yes means that this locus is associated with a CAD risk factor or another pleiotropic effect, provided in Supplemental Table I.

#eQTL effects are provided in Supplemental Table I.

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172 Arterioscler Thromb Vasc Biol February 2012

Figure 1. Post–genome-wide association study (GWAS) strategies, and biological and clinical implications of GWAS findings. The figure

depicts the different ongoing post-GWAS strategies to understand the mechanisms underlying the associations identified by GWAS

approach. The Manhattan plot showing the new coronary artery disease (CAD)/myocardial infarction (MI)–related loci that were identified

by CARDIoGRAM was previously used in Schunkert et al. 24 eQTL indicates expression quantitative trait locus; SNP, single-nucleotide

polymorphism.

progress in SNPs imputation 22 has allowed combining

GWAS data sets from different array platforms, and performing

large-scale analyses became feasible (Figure 1).

For CAD, the Coronary ARtery DIsease Genome-Wide

Replication Meta-Analysis (CARDIoGRAM) consortium

meta-analyzed 14 GWASs. 23,24 CARDIoGRAM included

22 233 cases and 64 762 controls. The pooled analysis of this

large sample replicated almost all previously identified common

genetic variants and led to identification of 13 new loci

with genome-wide significance (Table). 24 Using a more

focused study design, 2 additional loci were identified by

members of this consortium. 19,25

In addition to CARDIoGRAM, the International Consortium

for Coronary Artery Diseases (C4D) 26 is another largescale

meta-analysis of GWASs for CAD that was not limited

to populations of European descent. C4D identified 3 additional

new CAD/MI loci, which are listed in the Table. In the

future, the pooling of samples from the 2 consortia and

conducting even larger meta-analyses will probably allow

identification of additional variants with small effects.

Considering these 18 loci identified by CARDIoGRAM 24

and the C4D, 26 the 12 previously identified genetic vari-

ants, 11–13,17–19,21,25,27,28 a new locus identified by Wang et al 29

in a Han Chinese population, and a variant in the DAB2IP

gene identified by Gretarsdottir et al, 30 32 loci have now been

established as having genome-wide significance (Table).

CAD/MI loci identified by GWASs are characterized by

the following proprieties:

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1. Only a minority cover previously known CAD candidate

genes. Rather, most loci harbor genes that have not

been anticipated to be involved in the pathogenesis of

atherosclerosis (Table). An example for a new kid on

the block is ADAMTS7, which appears to be involved

in vascular smooth muscle cell migration and neointima

formation in balloon-injured rat arteries, 31 vascular

remodeling, 32 and the pathogenesis of arthritis. 33

2. Many loci are devoid of protein-coding genes. For some

variants, the signal is even located in gene deserts.

Herrington 7 suggests the existence of novel mechanisms

for genotype-phenotype interactions that are still not yet

understood. Indeed, the functional importance of gene

deserts needs to be reevaluated given these findings.

3. With the exception of 7q32.2 (ZC3HC1) and 15q25

(MORF4L1), most loci show pleiotropic effects (ie, they


are associated with several phenotypes). The most

pleiotropic loci are 11q23.3 (ZNF259-APOA5-APOA4-

APOA1-APOC3) and 9p21.3 (CDKN2A/CDKN2B),

which harbor 26 and 20 statistically significant associations

with various phenotypes, respectively. A variant

located 10 kb apart from the CAD/MI 9p21.3 susceptibility

locus within a different haplotype block was

found to be associated with type 2 diabetes. 34–36 This

risk locus was also found to be linked to abdominal

aortic aneurysm and intracranial aneurysms, 37 initiation

and progression of atherosclerosis, 38 stroke, 39 early

onset of MI, 13 vascular dementia and Alzheimer disease,

40,41 multiple cancer, 42 and premature death. In

addition, Musunuru et al 43 have shown recently that the

9p21.3 locus is associated with platelet reactivity. Although

these findings reveal that the complexity underlying

the genomic basis of human disease is much

greater than was initially thought, this pleiotropism may

be very helpful in understanding some mechanisms

shared among different diseases.

4. Most variants increase the probability of CAD and MI

only by a relatively small margin (5%–20%, as shown

in the Table). The largest effects are observed for the

risk alleles at 6q25.3 (LPA) and 9p21.3 (CDKN2A/

CDKN2B), which increase CAD risk by 51% and 29%

per allele, respectively. 24

5. Almost all CAD and MI loci were identified in European

populations. Although the 9p21.3 locus has been

replicated in Asian and in African American populations,

it is important to assess the generalizability of

results for the CAD/MI susceptibility loci in other

populations. 14–16

6. Genes mapped to CAD/MI susceptibility loci show significant

enrichment in lipoprotein metabolism, including

genes such as LDLR, LPA, PCSK9, PEMT, and the nearest

genes to the 11q23.3 locus (APOA1, APOA5, and

APOC3).

7. Many CAD/MI-associated loci show expression quantitative

trait locus (eQTL) effects, ie, they affect the

expression level of genes either in cis (at the locus) or in

trans (at remote locations) (Table). For example, SNP

rs1412444 (10q23.31) affects the expression of the

lipase A, lysosomal acid, cholesterol esterase (LIPA)

gene (cis eQTL) in liver and in monocytes and macrophages.

The association in monocyte cells was highly

significantly observed in the Gutenberg Heart Study

(P5.610 161 , as shown in Zeller et al 44 ) and in

Cardiogenics (P7.910 133 , Cardiogenics Consortium,

manuscript in preparation). rs599839 at the

1p13.3 locus (PSRC1) affects the expression of PSRC1

in monocytes, macrophages, whole blood, and liver.

The same genetic variant affects also the expression of

SORT1 and CELSR2 in liver (Supplemental Table I,

available online at http://atvb.ahajournals.org).

8. Figure 2 depicts a network for the CAD/MI-associated

genes, constructed from STRING, a database for known

and predicted protein-protein interactions. 45 Some nodes

(genes) are connected including mainly genes involved in

low-density lipoprotein (LDL) cholesterol and lipoprotein.

However, approximately 70% of the genes mapped to

CAD/MI do not form any known network of proteinprotein

interaction, cocitation in the literature, or coexpression

network (nodes without connections on Figure 2).

This suggests little functional interaction between these

Maouche and Schunkert GWASs for Atherosclerosis 173

genes. However, enriching the network by genes interacting

with each of the CAD/MI-associated genes may reveal

many further indirect interactions (links) and mechanisms

unifying these CAD genes into joint biological or cellular

processes. The currently known subnetwork involves

mainly genes involved in lipid and cholesterol metabolism

(Figure 2).

Recently, the heterogeneity in the presentation of CAD raised

the question whether distinct molecular mechanisms are involved

in specific phenotypes, such as calcification or left main

disease, and how this affects GWASs. 9,46 Indeed, it appears that

distinct coronary phenotypes, such as proximal stenosis or

calcifications, carry a high heritability. 47 Several authors have

investigated such subphenotypes of CAD, 25,48,49,50 providing

evidence that specific genetic predispositions promote the development

of either coronary atherosclerosis or MI. However,

the evidence for phenotype heterogeneity on the strength of

association at previously identified CAD susceptibility loci is

controversial. 48,49,50 Diemert and Schunkert 51 provide reflections

on these aspects and a list of CAD phenotypes that might have

distinct genetic etiology.

It is important to mention that in current GWASs of atherosclerosis,

CAD and MI cases are analyzed together, as there is

only limited evidence on the existence of genetic variants that

could be associated only with MI or only with CAD.

CAD Loci, Risk Factors, and

Disease Mechanisms

Once susceptibility genetic variants are identified by

GWASs, the next logical question is how these variants affect

disease risk. An obvious explanation may be by modification

of traditional CAD risk factors. In CARDIoGRAM, we found

only 3 variants associated with CAD risk factors in such

quantitative dimension that the risk factor induction may

explain the association with CAD (9q34.2 with total cholesterol

and LDL; 10q24.32 with hypertension; and 11q23.3

with LDL, high-density lipoprotein, and total cholesterol). In

this review, we report all associations found in GWAS

catalogue for the 32 CAD loci. Supplemental Table II lists for

each locus CAD risk factors or other traits with which this

locus displays an association. As shown by Supplemental

Table II, several additional loci are related to CAD risk

factors, including LDL cholesterol (1p13.3, 1p32.3, 9q34.2,

11q23.3, and 19p13.2), high-density lipoprotein cholesterol

(11q23.3,17p13.3, and 19p13.2), total cholesterol (1p13.3,

9q34.2, 11q23.3, and 19p13.2), body mass index (6p21.3 and

17q21.32), triglyceride (11q23.3), blood lipid trait (1p13.3),

obesity (6p21.31, 6q23.2 and 7q22.3), adiposity (1q41),

alcoholism (6p21.31), smoking behavior (9q34.2 and

15q25.1), diastolic blood pressure (12q24.12 and 17q21.32),

and systolic blood pressure (10q24.12 and 12q24.32). Although

many such effects at these loci are small and related

to genetic variants other than those causing CAD, this

enormous pleiotropism might be very helpful in understanding

the mechanisms underlying the identified loci.

Further Functional Implications From

GWASs for Atherosclerosis

Remarkable progress has been made in explaining molecular

mechanisms induced by CAD loci. 52,53 The lead SNP at the

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174 Arterioscler Thromb Vasc Biol February 2012

Figure 2. Interactions between genes mapped to coronary artery disease (CAD)/myocardial infarction (MI) susceptibility loci. The network,

which was constructed from the STRING database, shows the interactions (links; protein-protein interactions, cocitation in the

literature, coexpression) between genes (nodes) mapped to CAD/MI susceptibility loci. Lipase A, lysosomal acid, cholesterol esterase

(LIPA) is involved in cholesterol metabolism, but no interaction was predicted for this gene with the remaining ones forming a subnetwork

of CAD-associated genes involved in cholesterol metabolism.

9p21.3 locus is a common variant located 110 kb from 2

tumor suppressor genes, CDKN2A, CDKN2B, and 2 other

genes, CDK2B-AS1 (ANRIL) and MTAP. Visel et al 54 have

shown that deletion of the corresponding stretch of DNA in

mice results in dysregulation of these genes. These authors,

who also investigated possible in vivo effects of the risk allele

on early stages of atherogenesis, did not find significant

difference in fatty lesion formation. 54 It was suggested that

the variant linked to CAD may downregulate CDKN2A and

CDKN2B in mice, enhance vascular cell proliferation, and

lead to excessive aortic smooth muscle cell proliferation.

Holdt et al 55 also analyzed expression of genes at the 9p21.3

locus in human atherosclerotic plaque and demonstrated that

CDKN2A, CDKN2B, and MTAP are expressed in the smooth

muscle cells of normal human arteries. Musunuru et al 56

suggest that pleiotropic effects of the 9p21.3 locus on

CAD/MI may be mediated by influencing platelet reactivity.

Recently, Harismendy et al 57 have shown that in human

vascular endothelial cells, interferon- signaling activation

and STAT1 binding at ECAD9 strongly affect the structure of

chromatin and transcriptional regulation at the 9p21.3 locus. 57

It was also suggested that the 9p21.3 locus may influence

CDKN2B-AS1 (ANRIL) regulation, which in turn is supposed

to regulate CDKN2A and CDKN2B. This regulation may lead

to increase in cellular proliferation and predispose to atherosclerosis

58,59 although “the mystery of the 9p21.3 locus

remains wide open.” 52

In addition to 9p21.3, 1p13.3 (CELSR2-PSRC1-SORT1)

has been the focus of intense functional experiments. This

locus was initially identified to associate with CAD 12 before

Teslovich et al 60 revealed that this locus is the strongest hit

for LDL cholesterol out of 95 loci identified to associate with

plasma lipid levels. Recently, Musunuru et al 61 demonstrated

that the causal variant, which is associated with increase of

expression levels of SORT1 in the liver, is located within a

binding site for the transcription factor CCAAT/enhancer

binding protein. It was suggested that SORT1 regulates

hepatic lipoprotein production and that loss of expression of

this gene induces hypercholesterolemia and atherosclerotic

lesion formation. 62 By contrast, using knockout or overexpression

of Sortilin 1 protein (SORT1) in mice, Kjolby et al 63

demonstrated that lower levels of SORT1 expression are

associated to decreased level of LDL cholesterol and verylow-density

lipoprotein secretion, which is opposite the

conclusion of Musunuru et al, 61 who reported that lower

levels of SORT1 mRNA are associated with increase of LDL

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cholesterol and very-low-density lipoprotein secretion. Tall

and Ai 64 have provided some elements to explain the opposite

conclusions reported by Kjolby et al 63 and Musunuru et al. 61

The authors suggested that difference in experimental conditions

and knockout models used by the 2 groups could

explain their opposite findings. In addition, Dubé etal 65 have

provided an interesting discussion on recent mechanistic

studies of SORT1.

The MRAS locus (3q22.3) is known to play a role in

neuronal differentiation. Moreover, recent data suggest a role

in osteogenesis. It was also shown that MRAS is activated by

bone morphogenetic protein-2 signaling and that it participates

in the osteoblastic determination, differentiation, and

transdifferentiation under p38 mitogen-activated protein kinase

1 and mitogen-activated protein kinase 8 also known as

JNK regulation. Today, there is no known mechanism that

could explain the functional effects of the CAD-causing

variant.

ADAMTS-7 (15q25.1) has been suggested recently as novel

therapeutic target for vascular restenosis and atherogenesis,

as the enzyme affects the extracellular matrix of vascular

smooth muscle cells. 66

Among the newly identified CAD/MI loci, SMG6

(17p13.3) is known to be involved in nonsense-mediated

mRNA decay. 67 SRR, a gene in the region of the 17p13.3 risk

locus, was previously shown to be associated with cardiac

structure and function (aortic root size). 68 The RASD1 gene

mapped to the 17p11.2 locus, which encodes a member of the

Ras superfamily of small GTPases and is thought to play a

role in dexamethasone-induced alterations in cell morphology,

growth, and cell–extracellular matrix interactions.

PPAP2B is known to actively hydrolyze extracellular lysophosphatidic

acid and short-chain phosphatidic acid.

GIP is thought to induce insulin secretion 69 and to have

significant effects on fatty acid metabolism. Using gastric

inhibitory polypeptide receptor knockout mice, Yamada and

Seino 70 provided evidence that gastric inhibitory polypeptide

links overnutrition to obesity by acting on adipocytes.

No functional implications are known for TCF21 (6q23.2),

which encodes a transcription factor of the basic helix-loop-helix

family; HHIPL1 (14q32.2); ANKS1A (6p21.31); CYP17A1;

CNNM2; NT5C2 (10q24.32); SNF8 and UBE2Z (17q21.32);

ATP5G1 (17q21.32); SMCR3; PEMT; RAI1 (17p11.2); and

ZC3HC1 (7q32.2). Several ongoing functional studies are likely

to shed light on the direct or indirect mechanisms linking these

loci to atherosclerosis and its complications.

A detailed discussion of the functional implications from

the 12 CAD/MI-associated loci identified before the CAR-

DIoGRAM initiative is given in a recent review by Sivapalaratnam

et al. 71

Moving Beyond GWASs: Current

Post-GWAS Strategies

The main challenges are now to (1) identify additional

variants with moderate/small effects by performing further

large scale metaanalyses, (2) identify the causative variants

with strong effects and refine the architecture of identified

loci, and (3) elucidate the molecular mechanisms underlying

the association of the loci. In addition, it was suggested that

Maouche and Schunkert GWASs for Atherosclerosis 175

the remaining heritability for CAD and MI may be explained

by rare variants. 3 Ongoing efforts have been deployed to

perform next-generation whole-genome or targeted-exome

sequencing followed by large-scale genotyping (Figure 1).

Translating GWAS findings to clinical application, ie,

better prediction of CAD and MI and development of novel

therapeutic strategies, 72 is the ultimate goal of genetics

studies on CAD and MI, although this is still very challenging.

Glazer emphasized that the finding of GWASs has been

a revolution in biology rather than clinical medicine. 73 As

pointed out by Lander, 4 one of the exciting results of GWASs

is the evidence about the relevance of noncoding-regions in

our genome. Glazer 73 discussed the necessity of follow-up on

GWASs using multiple experimental methods and techniques

and establishing collaborations between different scientific

disciplines. He also emphasized the necessity of developing

new bioinformatics methods integrating different genomic

data to interpret GWAS findings.

Recently, several post-GWAS follow-up initiatives were

initiated. The National Institutes of Health and National

Cancer Institute launched the post-GWAS initiative (http://

grants.nih.gov/grants/guide/rfa-files/RFA-CA-09-002.html)

and funded several transdisciplinary research projects with

the aim of exploiting GWAS findings in cancer, which could

provide the basis for expediting clinical translation of the

findings. A similar initiative for CAD and related traits has

been launched by the National Heart, Lung, and Blood

Institute.

In term of methodology, several bioinformatics tools have

been proposed these last few years to prioritize the identified

variants for functional studies and predict of the functional

consequences of each variant. 74

Fine-Mapping and Genotyping of Variants

Identified by 1000 Genomes Project

It is important to keep in mind that variants identified by

GWASs may be not causal but in strong linkage disequilibrium

with the causal ones that are located in genes or the

region nearby. Identifying causal variants is one the challenges

in the post-GWAS era.

Fine-mapping is used for this purpose to derive more detailed

information about the region identified by GWAS analysis,

refine the location of each identified genetic variant, and eliminate

false-positive signals. It uses deep sequencing of the

surrounding region followed by genotyping of additional variants.

However, this approach is time-consuming, expensive, and

challenging, particularly when a locus of interest spans a large

genomic region. However, custom-made arrays such as the

MetaboChip (http://www.sph.umich.edu/csg/kang/MetaboChip/)

may facilitate this process. Availability of arrays that allow

combining GWASs and fine-mapping strategies, such as the

Affymetrix Axiom genotyping arrays (http://www.affymetrix.

com), will also help in identifying causal variants and refine

findings of GWAS analysis.

It has been suggested that studying different ethnic groups

with distinct haplotype patterns may help in the fine-mapping

approach. In addition, it has been suggested that data from the

1000 Human Genome Project 75 may be used to investigate

rare alleles without need for de novo sequencing. 73 Kim and

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176 Arterioscler Thromb Vasc Biol February 2012

Bhak 76 suggest that by typing the less frequent SNPs identified

by the 1000 Genomes Project directly in the study

samples, it will be possible to save the resequencing efforts in

the follow-up studies.

Sanna et al 77 have used fine-mapping of 5 loci associated

with LDL cholesterol by combining resequencing and genotyping

variants provided by the 1000 Genomes Project. 75 The

authors identified new functional variants and found that in 1

locus within the PCSK9 gene, the GWAS signal could be

explained by a low-frequency SNP. Shea et al 78 used a similar

approach and compared strategies for fine-mapping the association

of common variants at the 9p21.3 locus with type 2

diabetes and MI but did not identify new variants with

stronger associations than had already been discovered by the

GWAS approach.

Use of eQTL Data to Interpret GWAS Findings

As most of the common variants identified by GWASs are

located in noncoding regions, several authors have tried to

make use of eQTL data to interpret GWAS findings. 24,26

Specifically, variants located in a noncoding region may

affect transcript abundance of genes either in cis or in trans or

the splicing or stability of the transcript. Interestingly, some

variants show an eQTL effect in different tissues, as demonstrated

by Folkersen et al, 79 who examined the genetics of

gene expression of recently identified loci in different tissues.

Montgomery and Dermitzakis 80 nicely discussed how to

integrate and interpret overlap between GWAS and eQTL

studies. However, the joint interpretation is not evident at

some loci, as illustrated by Supplemental Figure I for the

MRAS locus (3q22.3). The initially identified variant

(rs9818870) was found to affect in cis the expression level of

MRAS in mammary artery tissue, in medial aortic tissues, and

in adventitial aortic tissues. However, in CARDIoGRAM, the

SNP showing the strongest effect was rs2306374 at the same

locus, which was found to affect the expression of FAIM

(3q22.3) in macrophages (Supplemental Figure I), leaving

open the question of whether MRAS or FAIM (or both)

mediates the disease risk.

In Vivo and In Vitro Functional Experiments

Depending on the characteristics of the chromosomal region,

different strategies are being used to elucidate the mechanisms

by which genetic variants lead to diseases. 81 For

nonsynonymous SNPs, specifically those mutations leading

to changes in the coded protein, it will be feasible to

functionally study both variants of the protein separately.

Transfection experiments and promoter analyses could be

used to study those variants located near regulatory elements.

In addition, the recent progress in developing transgenic

animal models will inform in vivo mechanistic studies.

However, animal based studies carry several limitations

mainly because of (1) their focus on single mutations, 82 (2)

differences between human and animal physiology, (3) differences

in the genetic structure and sequence of noncoding

regions, and (4) the duration of experimental models, which

are relatively short compared the development of pathological

processes in humans. 81

Use of Pathway-Based and SNP-Set

Enrichment Approaches

GWAS data are traditionally analyzed using single markers in

a SNP-by-SNP approach. Only variants which reach genomewide

statistical significance are being reported ignoring the

joint interplay of multiple variants with moderate effects.

Borrowing the strengths of gene-set enrichment analysis,

developed initially for microarray gene expression data,

recently SNP-set enrichment analysis (reviewed by Wang et

al 83 ) has been adapted for GWAS analysis to identify additional

diseases susceptibility variants by incorporating prior

biological knowledge and focusing on groups of functionally

related genes. Several research groups have reanalyzed their

GWAS data sets using SNP-set enrichment analysis approaches

(reviewed in Maouche et al, manuscript in preparation).

For CAD, our group started recently within the CAR-

DIoGRAM consortium a pathways analysis working group,

which may identify CAD-related pathways and provide novel

insights into the mechanisms of CAD and MI.

Systems Genetics Approaches and Network

Medicine to Integrate Multiple Levels of

Genomic Data

Systems genetics approaches, which integrate experimental

and high-throughput genomics data and computational methods,

will also play a growing role in merging all these data

into the analysis and interpretation of GWAS findings.

Recently, Nadeau and Dudley 84 nicely described this approach

and discussed how it can be used for dissection of

complex traits. Plaisier et al 85 have used an elegant systems

genetics approach to show that FADS3 is a causal gene for

familial combined hyperlipidemia (FCHL) and elevated triglycerides

in Mexicans. The authors used network gene

coexpression analysis and SNP data to assign a function to

the genetic variants rs3737787 (1q21-q23) in USF1 gene,

which was previously identified to be associated with FCHL.

Network medicine 86 will play an important role in these

analyses and is likely to advance our understanding of

pathophysiological mechanisms of diseases with complex

architecture, such as CAD and MI.

Epistasis and Gene-Environment Analysis

CAD and MI are complex diseases likely resulting from

interaction of several genetic and environmental factors.

Modeling gene-gene interactions and gene-environment interactions

(epistasis) is important for the next step of analysis

of GWAS data to understand how these factors interact with

each other. Several studies have illustrated the importance of

considering these interactions. Shirts et al 87 investigated

gene-age interaction in high-density lipoprotein, LDL, and

triglyceride levels and showed that the association between

SORT1 and LDL is age dependent. However, because of the

large number of studied markers, these interaction analyses

are still very challenging. A review by Thomas nicely

discusses the methodological aspects of gene-environment

interactions in GWAS analysis. 88

Targeted Capture of Exome and

Next-Generation Sequencing

The modest proportion of heritable risk for CAD and MI

currently explained by genetic variants raised the question of

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the nature of missing heritability. One source may be rare

variants (ie, variants with allelic frequency 1%), which are

not represented on commercial SNP arrays. 73,89 Nextgeneration

sequencing and whole-exome capture have been

demonstrated to be powerful approaches to studying mendelian

diseases. 90 Applying these techniques to complex diseases,

including CAD and MI, may lead to new insights into

the genetics basis of these diseases. Although it is likely that

next-generation sequencing will be a competitive alternative

to next-generation GWASs, because of the costs, it is still not

feasible to sequence a large number of individuals. Targeted

sequencing (eg, all exons in the genome) is likely to be an

intermediate accessible approach. To identify rare variants

associated to cardiovascular disease, the National Heart,

Lung, and Blood Institute has endorsed the GO Exome

Sequencing Project (http://esp.gs.washington.edu/drupal),

which is currently applying exome sequencing to well-phenotyped

cardiovascular cohorts. This initiative will provide

novel insights into whether the remaining heritability can be

explained by rare variants not included in the first generation

of GWASs.

Clinical Implications: Moving From Gene

Discovery to Clinical Translation and

Personalized Medicine

The ultimate goal of GWAS analysis and genomic medicine

is to use the newly identified variants for advanced clinical

strategies in management, prediction, and diagnostic and

prognostic evaluation, as well as personalized therapy. However,

in the context of complex disease, this is challenging for

many reasons: (1) the identified variants explain only a

limited fraction of disease risk; (2) currently identified risk

alleles often show a high prevalence in the population such

that virtually every subject carries a distinct set of multiple

risk variants; (3) precise molecular mechanisms, as well as

gene-gene and gene-environment interactions (epistasis), still

need to be discovered; and (4) GWAS findings are reported

mainly on European populations, such that additional studies

are required to identify new variants in other populations and

to assess the generalizability of current GWAS results.

Nevertheless, the whole field of atherosclerosis research

has been vitalized by GWAS findings. Ongoing studies are

likely to accelerate our understanding of the pathophysiological

mechanisms of CAD, which will fertilize clinical translation

and development of novel medical applications based

on GWAS findings.

Conclusion

Studying atherosclerosis using a GWAS approach has yielded

exciting new results and led to a rapid increase in the number

of CAD susceptibility loci. The related genes have shed new

light on the genetics architecture of CAD and MI and

advanced our understanding of the relationships between

genetic variability, disease risk factors, and pathophysiology

of CAD and MI. The mechanisms underlying the association

of these loci to CAD/MI remain largely unknown, and the

effects are relatively small. Future challenges are (1) discovering

further genetic variants through large-scale metaanalyses,

pathway-based approaches, and high throughput se-

Maouche and Schunkert GWASs for Atherosclerosis 177

quencing; (2) understanding the mechanisms linking the

identified loci to CAD; and (3) translating the findings from

CAD GWASs to novel and optimized therapeutic strategies

for CAD risk prediction, monitoring, and treatment.

Acknowledgments

We would like to thank all participants and investigators of the

CARDIoGRAM consortium and the German myocardial infarction

family studies. The authors would like to thank Ms Renate Domeier

for valuable assistance during the correction and preparation of this

manuscript.

Sources of Funding

We gratefully acknowledge support from the European Community

for Cardiogenics network (6th framework program; LSHM-CT-

2006-037593), the integrated project ENGAGE (201413) of the

European Union, the BMBF funded projects Atherogenomics (FKZ:

01GS0831), and the CARDomics (FKZ: 01KU0908B).

None.

Disclosures

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Supplement Material

Strategies beyond genome-wide association studies for

atherosclerosis

Seraya Maouche 1 1, *

and Heribert Schunkert

1 Universität zu Lübeck, Medizinische Klinik II, Ratzeburger Allee 160, D-23538 Lübeck,

Germany

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Supplemental Table I. eQTL effects of CAD/MI loci in different tissues.

Band SNP

(risk allele)

1p13.3

1p32.2

2q33.1

3q22.3

6q23.2

7q22.

3

9p21.3

10p11.23

rs646776

(T)/

rs599839

(A)

rs17114036

(A)

rs4675310

(A )/

rs6725887

(C)

rs9818870

(T)

rs2306374

(C)

rs12190287

(C)

rs10953541

(C)

rs4977574

(G)

rs2505083

(G)

Monocytes

rs599839-

PSRC1

(↑; P 5.3 < 10 -

55 ; cis eQTL;#)

rs646776-

PSRC1

(↑; P < 6.2 x

10 -20 ; cis

eQTL;ǂ)

Macrophages

PSRC1

(↑; P <

8.7 x 10 -

19 ;cis

eQTL;ǂ)

Cell/tissue

(direction of effect; P-value; type of eQTL; reference)

Lymphoblast

------ PSRC1

(↓; P <

2.4 x 10 -

24 ; cis

eQTL;**)

Blood Mammary

artery

tissue

Adventiti

al aortic

tissue

Medial

aortic

tissue

Carotid

plaque

tissue

------ ------ ------ ------ SORT1

(↑; P < 8.2

x10 -62 ; cis

eQTL; §)

------ ------ ------ ------ ------ ------ ------ PPAP2B

(↑; P < 10 -

rs4675310 -

FAM117B

(↓; P 1.4< 10 -

27 ; cis eQTL;#)

rs6725887-

FAM117B

(↓; P 4.4 x <

10 -19 ; cis

eQTL; ǂ)

------

------

------ ------ ------ NBEAL1

(↑; P < 4.3 x

10 -05 cis

eQTL; §)

------

FAIM

(↑; P <

6.8 x 10 -12

cis

eQTL;ǂ)

------

------

------

------

MRAS

(↓; P < 5.2

10 -16 cis

eQTL; §)

------

NBEAL1

(↑; P <

0.0003

cis

eQTL; §)

MRAS

(↓; P <

2.5 x 10 -

09 cis

eQTL; §)

------

NBEAL

1

(↑; P <

0.002

cis

eQTL;

§)

MRAS

(↓; P <

0.004

cis

eQTL;

§)

------

03 ;

cis

eQTL;*)

Liver Omental Subcutane

ous

adipose

tissue

PSRC1

(↑; P < 8.4 x

10 -27 ;

cis eQTL; §)

------ PSRC1

(↑; P < 1.3 x

10 -09 ;

cis eQTL;

**)

CELSR2

(↑; P < 3.6 x

10 -06 ;

cis eQTL;§)

------ ------ ------

------ ------ ------ ------

------ ------ ------ ------ ------ ------ ------ ------ TCF21

(↑; P 2.3 x<

10 -08 ;

cis eQTL;**)

------ ------ GPR22

(↑; P <

10 -05 ;

cis

eQTL;*)

------

------

------

------

------

------

TCF21

(↑; P 1.2

x< 10 -08 ;

cis

eQTL;**)

------ ------ ------ ------ ------ ------ ------ ------

------ ------ ------ ------ ------ ------ ------ ------ ------ CDKN2B

(↓; P 5 x

< 10-06

cis

eQTL; **)

-----

------

------

------

KIAA1462

(↑; P < 3.4 x

10 -07 ;

cis eQTL;*)

KIAA146

2

(↑; P <

6.4 x 10 -

03

;

cis

eQTL;*)

------

------

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------

------

------

hCT195150

5.1 (↓; P <

3.7 x 10 -06

cis eQTL;

**)

------

------

------


10q23.31

10q24.32

11q22.3

15q25

17p11.2

17q21.32

19p13.2

21q22.11

rs1412444

(T)

rs12413409

(G)

rs974819

(T)

rs4380028

(C)

rs12936587

(G)

rs46522

(T)

rs1122608

(G)

rs9982601

(T)

LIPA

(↑; P 5.6 x <

10 -161 ;

cis eQTL;#)

LIPA

(↑; P < 7.9 x

10 -133

cis eQTL;ǂ)

USMG5

(↑; P < 1.2 x

10 -52 ; cis

eQTL;#)

USMG5

(↑; P < 2.3 x

10 -21

cis eQTL;ǂ)

LIPA

(↑; P <

2.9 x 10 -

07 ; cis

eQTL;ǂ)

USMG5

(↑; P <

5.5 x 10 -

19 ; cis

eQTL;ǂ)

§ Folkersen et al. 1

# Zeller et al. 2

ǂ Cardiogenics Consortium (manuscript in preparation)

* C4D Consortium (2011) 3

**Schunkert et al. (2011) 4

------ ------ ------ ------ ------ ------ LIPA

(↑; P < 10 -05 ;

cis eQTL;*)

------ ------

------ ------ ------ ------ ------ ------ ------ ------ ------

------ ------ ------ ------ PDGFD

(↑; P < 7.2 x

10 -04 ;

cis eQTL;*)

------ ------ ADAMT

S7

(↑; P <

1.1 x 10 -

04 ;

cis

eQTL;*)

PDGFD

(↑; P <

7.7 x 10 -

04

;

cis

eQTL;*)

PDGFD

(↑;

P < 2.7

x 10 -07 ;

cis

eQTL;*)

------ ------ ------ ------

------ ------ ------ ------ ------ ------ ------ ------

------ ------ ------ ------ ------ ------ ------ ------ ------ ------ SMCR3

(↑; P < 10 -

UBE2Z

(↑; P < 3.8 x

10 -12 ; cis

eQTL; #)

------ UBE2Z

(↑; P <

10 -12 ;

cis

eQTL;**)

------ ------ ------ ------ ------ ------ ------ ------

------ ------ ------ ------ ------ ------ ------ ------ ------ SMARCA

4

(↑; P <

0.0003

cis

eQTL; **)

------ ------ ------ MRPS6

(↑; P <

10 -11 ;

cis

eQTL;**)

------ ------ SLC5A

3

(↑; P <

0.0003

cis

eQTL;

§)

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12 ;

cis eQTL;**)

------

------ ------ ------ ------


Supplemental Table II. Association of the 32 CAD/MI loci with CAD risk factors and other diseases. This table was

generated based on GWAS results available in the NHGRI GWAS Catalog 5 . Either the same genetic variant associated to

CAD/MI, SNP in LD or SNP within some distance of the CAD/MI associated SNP are considered to show the pleiotropic effects

at each locus. In the table, a cell in green corresponds to existence of association between a SNP at a given locus to a trait

shown on the first column. For details on each association, the reader can refer to the NHGRI GWAS Catalog

(http://www.genome.gov/gwastudies/).

Disease/trait 1p13.3 (CELSR2-PSRC1-

CAD/MI

Cardiac hypertrophy

Heart failure

Mortality among heart

failure patients

RR interval (heart rate)

Intracranial aneurysm

Type 2 diabetes

Type 1 diabetes

Diabetic retinopathy

Diabetic nephropathy

Adiposity

LDL cholesterol √

HDL cholesterol

Total cholesterol √

Blood lipid traits √

Body mass index

Obesity

Waist-hip ratio

Lipoprotein-associated

phospholipase A2

activity and mass √

Plasma Lp (a) levels

Triglycerides

Serum metabolites

Systolic blood pressure

Diastolic blood pressure

Blood Pressure

SORT1)

1p32.2 (PPAP2B)

1p32.3 (PCSK9)

1q41 (MIA3)

2q33.1 (WDR12)

3q22.3 (MRAS)

6p21.31 (ANKS1A)

6p24.1 (PHACTR1)

6q23.2 (C6orf105)

6q25.3 (TCF21)

7q22.3 (LPA)

7q32.2 (BCAP29)

9p21.3 (ZC3HC1)

9q33 (DAB2IP)

9q34.2 (CDN2A/B)

10p11.23 (KIAA1462)

√ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √








Hypertension √

Hypertriglyceridemia

Major CVD

Ventricular conduction

Response to statin

therapy √

Smoking behavior

Monocyte early

outgrowth colony

forming units













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10q11.21 (CXCL12)

10q23.31 (LIPA)



10q24.32 (CYP17A1)



11q22.3 (PDGFD)

11q23.3 (ZNF259)









12q24.12 (SH2B3)




13q34 (COL4A1/COL4A2)

14q32.2 (HHIPL1)


15q25 (MORF4L1)

15q25.1 (ADAMTS7)


17p11.2 (RASD1/SMCR3)

17p13.3 (SMG6/SRR)

√ √





17q21.32 (UBE2Z)




MRPS619p13.2 (LDLR)






21q22.11 ()MRPS6)


F-cell distribution

Colorectal cancer

Dupuytren's disease

Keloid

Optic disc size (cup)

Response to

antipsychotic therapy

(extrapyramidal side

effects)

Cognitive performance

Attention deficit

hyperactivity disorder

Hyperactive-impulsive

symptoms

Osteoporosis

Neutrophil count

Menarche (age at

onset)

Soluble ICAM-1

Cardiovascular disease

risk factors

Metabolic syndrome

Rheumatoid arthritis

Hematocrit

Hemoglobin

Alcoholism (alcohol

dependence factor

score)

Serum phosphorus

levels

Height

Testicular germ cell

tumor

Serum uric acid

Crohn's disease and

Celiac disease

Serum creatinine

Celiac disease

Prostate cancer

Serum markers of iron

status

Brain lesion load

Non-alcoholic fatty liver

disease histology (AST)

Protein quantitative

trait loci

Osteoarthritis

Warfarin maintenance

dose

Mean platelet volume

Menopause (age at

onset)































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√ √








Glaucoma

Glioma

D-dimer levels

Entorhinal cortical

thickness

Personality dimensions

Abdominal aortic

aneurysm

Endometriosis

Vertical cup-disc ratio

Nasopharyngeal

carcinoma

Breast cancer

Melanoma

Cutaneous nevi

Glioma (high-grade)

Quantitative traits

Asthma (toluene

diisocyanate-induced)

Serum phytosterol

levels

Plasma E-selectin levels

Soluble levels of

adhesion molecules

Hematological and

biochemical traits

Angiotensin-converting

enzyme activity

Serum soluble Eselectin

Pancreatic cancer

Venous

thromboembolism

Plasma levels of liver

enzymes

Non-alcoholic fatty liver

disease histology

(lobular)

Normalized brain

volume

Bipolar disorder and

schizophrenia

Emphysema-related

traits

Mean corpuscular

volume

Hirschsprung's disease

Parkinson's disease

Atrioventricular

conduction

Immunoglobulin A

Lung cancer

Other metabolic traits

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√ √







Celiac disease and

Rheumatoid arthritis

HIV-1 susceptibility

Brain structure

Cognitive test

performance

Pain

Plasma carotenoid and

tocopherol levels

Multiple sclerosis

(severity)

Drinking behavior

Retinal vascular caliber

Esophageal cancer

Plasma eosinophil

count

Glycated hemoglobin

levels

Serum bilirubin levels

Arterial stiffness

Factor VII

Radiation response

Optic disc size (disc)

Tanning

Hemostatic factors and

hematological

phenotypes

Refractive error

Chronic obstructive

pulmonary disease

Primary biliary cirrhosis

Serum matrix

metalloproteinase

Exercise treadmill test

traits

Multiple sclerosis

Serum calcium

Aortic root size

Sphingolipid levels

Lung adenocarcinoma

Inattentive symptoms

Bipolar disorder

Nicotine dependence

Response to metformin

Small-cell lung cancer

Common traits (Other)

AIDS progression

Psoriasis

Primary tooth

development

√ √

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√ √




































Information processing

speed

Amyotrophic lateral

sclerosis

Vitiligo

Reasoning

Ovarian cancer

Progranulin levels √

Working memory √

Anorexia nervosa √

Self-rated health √

Kawasaki disease

AB1-42

HIV-1 control

Systemic lupus

erythematosus

Paget's disease √

Chronic kidney disease

Major depressive


disorder √

AIDS √

Panic disorder √

Dialysis-related

mortality

Crohn's disease

Ulcerative colitis

Response to

antipsychotic treatment

Exercise (leisure time)

Response to treatment

for acute lymphoblastic

leukemia

Attention deficit

hyperactivity disorder

and conduct disorder

Hip bone size

MRI atrophy measures

Acenocoumarol

maintenance dosage

Fibrinogen

Hip geometry
















√ √






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Supplemental Figure I. Complexity of the genetic structure for some CAD/MI associated loci. The figure

depicts eQTL effects of two genetic variants in the MRAS gene which are associated to CAD/MI. The

initially identified variant (rs9818870) was found to affect in cis the expression level of MRAS in mammary

artery tissue, in medial aortic tissues and in adventitial aortic tissues (as shown on the green boxplot

obtained from Folkersen et al. 1 ). The risk allele (T) is associated with decrease in the expression level of

MRAS gene. However, in CARDIoGRAM the SNP showing the strongest CAD effect was rs2306374 at the

same locus which was found to affect the expression of FAIM (3q22.3) in macrophages (grey boxplot). The

risk allele C (G on the boxplot) is associated with increase in the expression level of FAIM.

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Supplemental references

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Syvänen AC, Paulsson-Berne G, Paulssson-Berne G, Franco-Cereceda A, Hamsten A,

Gabrielsen A, Eriksson P, groups BaAs. Association of genetic risk variants with expression of

proximal genes identifies novel susceptibility genes for cardiovascular disease. Circ

Cardiovasc Genet. 2010;3:365-373

2. Zeller T, Wild P, Szymczak S, Rotival M, Schillert A, Castagne R, Maouche S, Germain M,

Lackner K, Rossmann H, Eleftheriadis M, Sinning CR, Schnabel RB, Lubos E, Mennerich D, Rust

W, Perret C, Proust C, Nicaud V, Loscalzo J, Hubner N, Tregouet D, Munzel T, Ziegler A, Tiret

L, Blankenberg S, Cambien F. Genetics and beyond--the transcriptome of human monocytes

and disease susceptibility. PLoS One. 2010;5:e10693

3. C4D Consortium. A genome-wide association study in europeans and south asians identifies

five new loci for coronary artery disease. Nat Genet. 2011;43:339-344

4. Schunkert H, Konig IR, Kathiresan S, Reilly MP, Assimes TL, Holm H, Preuss M, Stewart AF,

Barbalic M, Gieger C, Absher D, Aherrahrou Z, Allayee H, Altshuler D, Anand SS, Andersen K,

Anderson JL, Ardissino D, Ball SG, Balmforth AJ, Barnes TA, Becker DM, Becker LC, Berger K,

Bis JC, Boekholdt SM, Boerwinkle E, Braund PS, Brown MJ, Burnett MS, Buysschaert I,

Carlquist JF, Chen L, Cichon S, Codd V, Davies RW, Dedoussis G, Dehghan A, Demissie S,

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